Evaluation of carbohydrate concentrations in Phalaenopsis using near-infrared spectroscopy

Yung Kun Chuang, I. Chang Yang, Chao Yin Tsai, Jiunn Yan Hou, Yung Huei Chang, Suming Chen

Research output: Contribution to journalArticlepeer-review


Carbohydrate concentrations are important indicators of the internal quality of Phalaenopsis. Inthisstudy, near-infrared (NIR) spectroscopy was used for quantitative analyses of fructose, glucose, sucrose, and starch in Phalaenopsis plants. Both modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR) methods were used for spectral analysis of 302 Phalaenopsis samples in the full visible NIR wavelength range (400–2498 nm). Calibration models built by MPLSR were better than those built by SMLR. For fructose, the smoothed first derivative MPLSR model provided the best results, with a correlation coefficient of calibration (Rc) of 0.96, standard error of calibration (SEC) of 0.22% dry weight (DW), standard error of validation (SEV) of 0.28% DW, and bias of-0.01% DW. For glucose, the MPLSR model based on the smoothed first derivative spectra was the best (Rc =0.96;SEC= 0.26% DW; SEV = 0.32% DW; and bias = 0.01% DW). The best MPLSR model of sucrose was developed using the smoothed first derivative spectra (Rc = 0.96; SEC = 0.24% DW; SEV = 0.31% DW; bias =-0.03% DW). Regarding starch, the smoothed first derivative MPLSR model showed the best effects (Rc = 0.91; SEC = 0.47% DW; SEV = 0.56% DW; bias =-0.02% DW). Both the MPLSR and SMLR models showed satisfactory predictability, indicating that NIR has the potential to be adopted as an effective method of rapid and accurate inspection of the carbohydrate concentrations of Phalaenopsis plants. This technique could contribute substantially to quality management of Phalaenopsis.

Original languageEnglish
Pages (from-to)494-502
Number of pages9
JournalJournal of the American Society for Horticultural Science
Issue number6
Publication statusPublished - Nov 1 2018


  • Fructose
  • Glucose
  • Internal quality
  • Modified partial least-squares regression
  • Quantitative analysis
  • Starch
  • Stepwise multiple linear regression
  • Sucrose

ASJC Scopus subject areas

  • Genetics
  • Horticulture


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